Face Gender Recognition Research Articles

Relative tuning of holistic face processing towards the fovea

Humans quickly detect and gaze at faces in the world, which reflects their importance in cognition and may lead to tuning of face recognition toward the central visual field. Although sometimes reported, foveal selectivity in face processing is debated: brain imaging studies have found evidence for a central field bias specific to faces, but behavioral studies have found little foveal selectivity in face recognition. These conflicting results are difficult to reconcile, but they could arise from stimulus-specific differences. Recent studies, for example, suggest that individual faces vary in the degree to which they require holistic processing. Holistic processing is the perception of faces as a whole rather than as a set of separate features. We hypothesized that the dissociation between behavioral and neuroimaging studies arises because of this stimulus-specific dependence on holistic processing. Specifically, the central bias found in neuroimaging studies may be specific to holistic processing. Here, we tested whether the eccentricity-dependence of face perception is determined by the degree to which faces require holistic processing. We first measured the holistic-ness of individual Mooney faces (two-tone shadow images readily perceived as faces). In a group of independent observers, we then used a gender discrimination task to measured recognition of these Mooney faces as a function of their eccentricity. Face gender was recognized across the visual field, even at substantial eccentricities, replicating prior work. Importantly, however, holistic face gender recognition was relatively tuned—slightly, but reliably stronger in the central visual field. Our results may reconcile the debate on the eccentricity-dependance of face perception and reveal a spatial inhomogeneity specifically in the holistic representations of faces.

Real-Time Gender Recognition for Juvenile and Adult Faces

Facial gender recognition is a crucial research topic due to its comprehensive use cases, including a demographic gender survey, visitor profile identification, targeted advertisement, access control, security, and surveillance from CCTV. For these real-time applications, the face of a person can be oriented to any angle from the camera axis, and the person can be of any age group, including juveniles. A child's face consists of immature craniofacial feature points in texture and edge compared to an adult face, making it very hard to recognize gender using the child's face. Real-word faces captured in an unconstrained environment make the gender prediction system more complex to identify correctly due to orientation. These factors reduce the accuracy of the existing state-of-the-art models developed so far for real-time facial gender prediction. This paper presents the novelty of facial gender recognition for juveniles, adults, and unconstrained-oriented faces. The progressive calibration network (PCN) detects rotation-invariant faces in the proposed model. Then, a Gabor filter is applied to extract unique edge and texture features from the detected face. The Gabor filter is invariant to illumination and produces texture and edge features with redundant feature coefficients in large dimensions. Gabor has drawbacks such as redundancy and a large dimension resolved by the proposed meanDWT feature optimization method, which optimizes the system's accuracy, the size of the model, and computational timing. The proposed feature engineering model is classified with different classifiers such as Naïve Bayes, Logistic Regression, SVM with linear, and RBF kernel. Its results are compared with the state-of-the-art techniques; detailed experimental analysis is presented and concluded to support the argument. We also present a review of approaches based on conventional and deep learning methods with their pros and cons for facial gender recognition on different datasets available for facial gender recognition.

A Fully Bayesian Inference with Gibbs Sampling for Finite and Infinite Discrete Exponential Mixture Models

ABSTRACT In this paper, we propose clustering algorithms based on finite mixture and infinite mixture models of exponential approximation to the Multinomial Generalized Dirichlet (EMGD), Multinomial Beta-Liouville (EMBL) and Multinomial Shifted-Scaled Dirichlet (EMSSD) with Bayesian inference. The finite mixtures have already shown superior performance in real data sets clustering using the Expectation–Maximization approach. The proposed approaches in this paper are based on a Monte Carlo simulation technique namely Gibbs sampling algorithm including an additional Metropolis–Hastings step, and we utilize exponential family conjugate prior information to construct their posterior relying on Bayesian theory. Furthermore, we also present the infinite models based on Dirichlet processes, which results in clustering algorithms that do not require the specification of the number of mixture components to be given in advance and selects it in a principled manner. The performance of our Bayesian approaches was evaluated in some challenging real-world applications concerning text sentiment analysis, fake news detection, and human face gender recognition.

Face Gender Recognition in the Wild: An Extensive Performance Comparison of Deep-Learned, Hand-Crafted, and Fused Features with Deep and Traditional Models

Face gender recognition has many useful applications in human–robot interactions as it can improve the overall user experience. Support vector machines (SVM) and convolutional neural networks (CNNs) have been used successfully in this domain. Researchers have shown an increased interest in comparing and combining different feature extraction paradigms, including deep-learned features, hand-crafted features, and the fusion of both features. Related research in face gender recognition has been mostly restricted to limited comparisons of the deep-learned and fused features with the CNN model or only deep-learned features with the CNN and SVM models. In this work, we perform a comprehensive comparative study to analyze the classification performance of two widely used learning models (i.e., CNN and SVM), when they are combined with seven features that include hand-crafted, deep-learned, and fused features. The experiments were performed using two challenging unconstrained datasets, namely, Adience and Labeled Faces in the Wild. Further, we used T-tests to assess the statistical significance of the differences in performances with respect to the accuracy, f-score, and area under the curve. Our results proved that SVMs showed best performance with fused features, whereas CNN showed the best performance with deep-learned features. CNN outperformed SVM significantly at p < 0.05.

Using Convolutional Neural Networks Based on a Taguchi Method for Face Gender Recognition

In general, a convolutional neural network (CNN) consists of one or more convolutional layers, pooling layers, and fully connected layers. Most designers adopt a trial-and-error method to select CNN parameters. In this study, an AlexNet network with optimized parameters is proposed for face image recognition. A Taguchi method is used for selecting preliminary factors and experiments are performed through orthogonal table design. The proposed method filters out factors that are significantly affected. Finally, experimental results show that the proposed Taguchi-based AlexNet network obtains 87.056% and 98.72% average accuracy of image gender recognition in the CIA and MORPH databases, respectively. In addition, the average accuracy of the proposed Taguchi-based AlexNet network is 1.576% and 3.47% higher than that of the original AlexNet network in CIA and MORPH databases, respectively.

Using Feature Fusion and Parameter Optimization of Dual-input Convolutional Neural Network for Face Gender Recognition

In recent years, convolutional neural networks (CNNs) have been successfully used in image recognition and image classification. General CNNs only use a single image as feature extraction. If the quality of the obtained image is not good, it is easy to cause misjudgment or recognition error. Therefore, this study proposes the feature fusion of a dual-input CNN for the application of face gender classification. In order to improve the traditional feature fusion method, this paper also proposes a new feature fusion method, called the weighting fusion method, which can effectively improve the overall accuracy. In addition, in order to avoid the parameters of the traditional CNN being determined by the user, this paper uses a uniform experimental design (UED) instead of the user to set the network parameters. The experimental results show that in the dual-input CNN experiment, average accuracy rates of 99.98% and 99.11% on the CIA and MORPH data sets are achieved, respectively, which is superior to the traditional feature fusion method.

The Upper Third in Facial Gender Confirmation Surgery: Forehead and Hairline.

The upper third of the face contains 2 features that are particularly important for facial gender recognition: the frontonasoorbital region and the hairline. The supraorbital ridge, which determines the position and exposure of the eyebrows, is almost invariably more developed in the male than in the female. Surgical modification of the frontonasoorbital complex, considered a standard procedure in facial feminization, is reliable and predictable, and also delivers satisfactory results that are stable over time.A prototypical male hairline has an M-shaped pattern compared to the more rounded shape often seen in female hairlines. Feminization of the hairline requires minimizing the temples as well as rounding out the overall shape, optimizing hair density, and occasionally changing the height of the hairline.This article provides an update on our forehead reconstruction technique and our experience in the treatment of hairline redefinition.

FDGRS: Improve Accuracy of Face Detection and Gender Recognition in Complex Lighting Environment by Image Enhancement Techniques

FDGRS: Improve Accuracy of Face Detection and Gender Recognition in Complex Lighting Environment by Image Enhancement Techniques

Convolutional neural networks based on multi-scale additive merging layers for visual smoke recognition

Traditional smoke recognition methods are mainly based on handcrafted features. However, it is difficult to design handcrafted features that are robust and discriminative for smoke recognition because of large variations in smoke color, shapes and textures. To solve this problem, we specifically design a basic block of convolutional neural networks (CNNs) and stack basic blocks to propose a novel deep multi-scale CNN (DMCNN) for smoke recognition. The basic block consists of several parallel convolutional layers with the same number of filters but different kernel sizes for scale invariance. Each convolutional layer is followed by a batch normalization to normalize the output of the convolutional layer. Then the basic block sums up all normalized outputs from multi-scale parallel layers and activates the sum as the final output of the block. To fully extract scale invariant features, we cascade eleven basic blocks, which is followed by a global average pooling and a 2D fully connected layer, to construct DMCNN. Experimental results show that our method achieves higher detection rates, higher accuracy rates and lower false alarm rates than existing methods. To further verify the efficiency of DMCNN, we also conducted face gender recognition experiments on the LFW database and our model also achieves obviously higher accuracy rates than other methods. Furthermore, our method is an efficient, lightweight CNN model with about 1 M parameters that are far less than other CNN methods.

Facial Gender Recognition via Low-rank and Collaborative Representation in An Unconstrained Environment

Facial Gender Recognition via Low-rank and Collaborative Representation in An Unconstrained Environment

FASTA-ELM: A fast adaptive shrinkage/thresholding algorithm for extreme learning machine and its application to gender recognition

Extreme learning machine (ELM) is an interesting algorithm for learning the hidden layer of single layer feed forward neural networks. However, one of the main shortcomings restricting further improvement of ELM is the complexity of singular value decomposition (SVD) for computing the Moore-Penrose generalized inverse of the hidden layer matrix. This paper presents a new algorithm named fast adaptive shrinkage/thresholding algorithm ELM (FASTA-ELM) which uses an extension of forward-backward splitting (FBS) to compute the smallest norm of the output weights in ELM. The proposed FASTA-ELM algorithm is evaluated on face gender recognition problem using 5 benchmarked datasets. The results indicate that FASTA-ELM provides efficient performance and outperforms the standard ELM and two other variants of ELM in terms of generalization ability and computational time. Furthermore, the recognition performance of FASTA-ELM is comparable to other state-of-the-art face gender recognition methods.

Compass local binary patterns for gender recognition of facial photographs and sketches

This paper presents a new feature extraction method, compass local binary pattern (CoLBP) for facial gender recognition. To achieve robustness, the proposed method first computes directional edge responses using eight Kirsch compass masks. Then, the spatial relationships among the neighboring pixels in each edge response are exploited independently with the help of local binary pattern (LBP) to enhance the discrimination capability. Finally, spatial histograms computed from these LBP images are concatenated to build a face descriptor. Our proposed descriptor efficiently extracts discriminating information from four different levels, including gradient, regional, global and directional level. The proposed method was evaluated on three datasets (color FERET, LFW, and Adience) containing facial photographs. In spite of a wide range of challenges (low resolution, variations in pose, expression, and illumination) present in the datasets, the proposed method provided promising classification performance in comparison with several existing benchmark methods, thereby validating its robustness. Moreover, this paper also investigates the gender recognition of facial sketches. The experiments carried out on two facial sketch datasets including CUFS and CUFSF also demonstrated better classification performance of the proposed method.

A review of facial gender recognition

Applications such as human---computer interaction, surveillance, biometrics and intelligent marketing would benefit greatly from knowledge of the attributes of the human subjects under scrutiny. The gender of a person is one such significant demographic attribute. This paper provides a review of facial gender recognition in computer vision. It is certainly not a trivial task to identify gender from images of the face. We highlight the challenges involved, which can be divided into human factors and those introduced during the image capture process. A comprehensive survey of facial feature extraction methods for gender recognition studied in the past couple of decades is provided. We appraise the datasets used for evaluation of gender classification performance. Based on the results reported, good performance has been achieved for images captured under controlled environments, but certainly there is still much work that can be done to improve the robustness of gender recognition under real-life environments.

Adaptive Face Gender Recognition Based on NSGA-II and Compressive Sensing

In the face gender recognition system, in addition to want to obtain high recognition rate, but also want to be able to achieve recognition quickly which relates to multi-objective optimization problem. In this paper, we proposed an adaptive face gender recognition method based on NSGA-II which can make recognition performance and running time optimal at the same time. In our method, we use CS to extract features and Extreme Learning Machine (ELM) to achieve features recognition. Then, NSGA-II is used to optimize the method. Experimental results show that the proposed method using NSGA-II solving optimization problems can obtain evenly distributed Pareto optimal solution set at a fast convergence in the solution space which demonstrates feasibility and effectiveness of using NSGA-II to solve multi-objective face gender recognition problems.

Learning to classify gender from four million images

The application of learning algorithms to big datasets has been identified for a long time as an effective way to attack important tasks in pattern recognition, but the generation of large annotated datasets has a significant cost. We present a simple and effective method to generate a classifier of face images, by training a linear classification algorithm on a massive dataset entirely assembled and labelled by automated means. In doing so, we perform the largest experiment on face gender recognition so far published, reporting the highest performance yet. Four million images and more than 60,000 features are used to train online classifiers. By using an ensemble of linear classifiers, we achieve an accuracy of 96.86% on the most challenging public database, labelled faces in the wild (LFW), 2.05% higher than the previous best result on the same dataset (Shan, 2012). This result is relevant both for the machine learning community, addressing the role of large datasets, and the computer vision community, providing a way to make high quality face gender classifiers. Furthermore, we propose a general way to generate and exploit massive data without human annotation. Finally, we demonstrate a simple and effective adaptation of the Pegasos that makes it more robust.

Face Gender Recognition Research Based on Local Features and Support Vector Machine

In this paper, we proposed a face gender recognition method based on local features and SVM. First, we divide the face image into five parts which are used to instead of the whole face for better recognition performance. Second, we use CS to extract local features of these five parts. Then, we respectively train five single SVM classifiers to achieve one to one feature recognition for local features. Finally, decision information fusion is used to achieve the final classification. Because SVM were successfully used to solve numerous pattern recognition problems and is mainly used to solve two-classification problem, selecting SVM to do gender recognition in our method has the obvious superiority. After a lot of experiments, results show that the proposed method in this paper is stable and effective, greatly improving the efficiency of face gender recognition.

Research on Adaptive Face Gender Recognition Based on Compressive Sensing

An adaptive gender recognition method is proposed in this paper. At first, do multiwavlet transform to face image and get its low frequency information, then do feature extraction to the low frequency information using compressive sensing (CS), use extreme learning machine (ELM) to achieve gender recognition finally. In the process of feature extraction, we use genetic algorithm (GA) to get the number of measurements of CS in order to gain the highest recognition rate, so the method can adaptive access optimal performance. Experimental results show that compared with PDA and LDA, the new method improved the recognition accuracy substantially.

Face recognition in simulated prosthetic vision: face detection-based image processing strategies

Objective. Given the limited visual percepts elicited by current prosthetic devices, it is essential to optimize image content in order to assist implant wearers to achieve better performance of visual tasks. This study focuses on recognition of familiar faces using simulated prosthetic vision. Approach. Combined with region-of-interest (ROI) magnification, three face extraction strategies based on a face detection technique were used: the Viola–Jones face region, the statistical face region (SFR) and the matting face region. Main results. These strategies significantly enhanced recognition performance compared to directly lowering resolution (DLR) with Gaussian dots. The inclusion of certain external features, such as hairstyle, was beneficial for face recognition. Given the high recognition accuracy achieved and applicable processing speed, SFR-ROI was the preferred strategy. DLR processing resulted in significant face gender recognition differences (i.e. females were more easily recognized than males), but these differences were not apparent with other strategies. Significance. Face detection-based image processing strategies improved visual perception by highlighting useful information. Their use is advisable for face recognition when using low-resolution prosthetic vision. These results provide information for the continued design of image processing modules for use in visual prosthetics, thus maximizing the benefits for future prosthesis wearers.

Real-time moustache detection by combining image decolorization and texture detection with applications to facial gender recognition

Real-time moustache detection by combining image decolorization and texture detection with applications to facial gender recognition

Fast Face Gender Recognition by Using Local Ternary Pattern and Extreme Learning Machine

Human face gender recognition requires fast image processing with high accuracy. Existing face gender recognition methods used traditional local features and machine learning methods have shortcomings of low accuracy or slow speed. In this paper, a new framework for face gender recognition to reach fast face gender recognition is proposed, which is based on Local Ternary Pattern (LTP) and Extreme Learning Machine (ELM). LTP is a generalization of Local Binary Pattern (LBP) that is in the presence of monotonic illumination variations on a face image, and has high discriminative power for texture classification. It is also more discriminate and less sensitive to noise in uniform regions. On the other hand, ELM is a new learning algorithm for generalizing single hidden layer feed forward networks without tuning parameters. The main advantages of ELM are the less stringent optimization constraints, faster operations, easy implementation, and usually improved generalization performance. The experimental results on public databases show that, in comparisons with existing algorithms, the proposed method has higher precision and better generalization performance at extremely fast learning speed.